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Event Information

  • The CreateFunction event in AWS for Lambda refers to the action of creating a new Lambda function.
  • This event is triggered when a user or an automated process initiates the creation of a Lambda function in AWS.
  • The CreateFunction event typically includes information such as the function name, runtime environment, handler, and other configuration settings for the Lambda function.

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Examples

  • Unauthorized access to the Lambda function: If proper IAM roles and permissions are not set up, there is a risk of unauthorized access to the Lambda function. This can lead to potential security breaches and unauthorized execution of code.
  • Insecure handling of sensitive data: If sensitive data, such as API keys or database credentials, are hardcoded within the Lambda function code or passed as plain text in environment variables, it can pose a security risk. This can potentially expose the sensitive information to unauthorized users.
  • Lack of encryption: If data transmitted to and from the Lambda function is not encrypted, it can be intercepted and accessed by malicious actors. It is important to ensure that data in transit and at rest is properly encrypted to maintain the security and integrity of the function.

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Remediation

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Using Console

  1. Identify the specific issue or vulnerability in the AWS Lambda function by reviewing the event logs or security findings in the AWS console.
  2. Determine the appropriate remediation steps based on the examples provided in the previous response: a. Example 1: Excessive permissions for Lambda function
    • Access the AWS Lambda console.
    • Select the specific Lambda function that has excessive permissions.
    • Click on the “Permissions” tab.
    • Review the existing permissions and identify any unnecessary or excessive permissions.
    • Remove the unnecessary permissions by clicking on the “X” icon next to each permission.
    • Click on “Save” to apply the changes.
    b. Example 2: Insecure environment variables in Lambda function
    • Access the AWS Lambda console.
    • Select the specific Lambda function that has insecure environment variables.
    • Click on the “Configuration” tab.
    • Scroll down to the “Environment variables” section.
    • Review the existing environment variables and identify any sensitive information.
    • Remove or encrypt any sensitive environment variables.
    • Click on “Save” to apply the changes.
    c. Example 3: Unencrypted data storage in Lambda function
    • Access the AWS Lambda console.
    • Select the specific Lambda function that has unencrypted data storage.
    • Click on the “Configuration” tab.
    • Scroll down to the “Environment variables” section.
    • Review the existing environment variables and identify any sensitive data storage configurations.
    • Modify the data storage configuration to use encryption, such as enabling server-side encryption for S3 buckets or using encrypted database storage.
    • Click on “Save” to apply the changes.
  3. Validate the remediation by re-evaluating the Lambda function’s security posture and ensuring that the identified issues have been resolved. Monitor the function for any further security events or vulnerabilities.

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Using CLI

  1. Enable VPC configuration for AWS Lambda:
    • Use the update-function-configuration command to update the Lambda function’s configuration.
    • Specify the --vpc-config parameter with the appropriate VPC configuration details, such as SubnetIds and SecurityGroupIds.
    • Example command: aws lambda update-function-configuration --function-name <function-name> --vpc-config SubnetIds=<subnet-ids>,SecurityGroupIds=<security-group-ids>
  2. Enable encryption at rest for AWS Lambda function code:
    • Use the update-function-configuration command to update the Lambda function’s configuration.
    • Specify the --kms-key-arn parameter with the ARN of the KMS key to be used for encryption.
    • Example command: aws lambda update-function-configuration --function-name <function-name> --kms-key-arn <kms-key-arn>
  3. Enable AWS CloudTrail logging for AWS Lambda:
    • Use the update-function-configuration command to update the Lambda function’s configuration.
    • Specify the --tracing-config parameter with the appropriate tracing configuration details, such as Mode set to Active.
    • Example command: aws lambda update-function-configuration --function-name <function-name> --tracing-config Mode=Active

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Using Python

  1. Example 1: Limiting Execution Time
  • Set a timeout value for your AWS Lambda function to ensure it doesn’t exceed the maximum allowed execution time.
  • In your Python script, you can use the signal module to handle timeouts. Here’s an example:
import signal

def lambda_handler(event, context):
    # Set the timeout value in seconds
    timeout = 5

    # Define a function to handle timeouts
    def timeout_handler(signum, frame):
        raise Exception("Function timed out")

    # Set the timeout handler
    signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(timeout)

    # Your code here
    # ...

    # Reset the alarm
    signal.alarm(0)

    # Return the response
    return {
        'statusCode': 200,
        'body': 'Function executed successfully'
    }
  1. Example 2: Enforcing Least Privilege
  • Implement AWS Identity and Access Management (IAM) policies to restrict the permissions of your Lambda function.
  • In your Python script, you can use the boto3 library to interact with IAM and attach the appropriate policies. Here’s an example:
import boto3

def lambda_handler(event, context):
    # Create an IAM client
    iam = boto3.client('iam')

    # Define the IAM policy
    policy_document = {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "s3:GetObject"
                ],
                "Resource": "arn:aws:s3:::my-bucket/*"
            }
        ]
    }

    # Attach the policy to the Lambda execution role
    iam.put_role_policy(
        RoleName='my-lambda-role',
        PolicyName='my-lambda-policy',
        PolicyDocument=json.dumps(policy_document)
    )

    # Your code here
    # ...

    # Return the response
    return {
        'statusCode': 200,
        'body': 'Function executed successfully'
    }
  1. Example 3: Encrypting Sensitive Data
  • Use AWS Key Management Service (KMS) to encrypt and decrypt sensitive data within your Lambda function.
  • In your Python script, you can utilize the boto3 library to interact with KMS and perform encryption and decryption operations. Here’s an example:
import boto3

def lambda_handler(event, context):
    # Create a KMS client
    kms = boto3.client('kms')

    # Encrypt sensitive data
    response = kms.encrypt(
        KeyId='my-kms-key',
        Plaintext='my-sensitive-data'
    )
    encrypted_data = response['CiphertextBlob']

    # Decrypt the encrypted data
    response = kms.decrypt(
        CiphertextBlob=encrypted_data
    )
    decrypted_data = response['Plaintext']

    # Your code here
    # ...

    # Return the response
    return {
        'statusCode': 200,
        'body': 'Function executed successfully'
    }
}